Learn AI /Search, Embeddings and RAG /Semantic similarity
Semantic similarity
- Starter
- Search, Embeddings and RAG
- Programme & data teams
Also called: similarity, meaning similarity.
Back to the milestone you came from
In five seconds
What it is
A measure of how close two pieces of text are in meaning, even if they use different words.
In plain words
It is a way to estimate whether two pieces of text mean nearly the same thing, even when they do not use the same words.
An everyday example
Amina has a box of handwritten notes from colleagues in Nairobi, Bangkok and Bogotá about community meetings. One note says “parents asked for evening sessions”, another says “families prefer meetings after work”, and a third says “bring chairs to the hall”. She wants notes about the same idea, even when the wording differs, so she puts similar notes into the same tray. The first two notes end up together because they point to a similar meaning, while the chair note goes elsewhere.
A person can use background knowledge and context that a system may not have, such as tone, irony or local customs. Real systems usually do not literally sort papers into trays. They compare mathematical representations of text, and their similarity scores can be wrong or biased if the training data or text context is weak.
Remember thisSemantic similarity ranks text by estimated closeness of meaning, not identical wording. A high score is a retrieval clue, not proof that two records are equivalent.
Understand it
Understand it
Why it matters
For non-specialists, semantic similarity matters because it affects what information you see first. If a system can match by meaning, it can find useful passages, past cases or reports that use different wording from your question. That can make search, support tools and retrieval-augmented generation more useful. But it also affects trust: a high similarity score does not mean a passage is correct, current or appropriate. Knowing this helps you ask better questions, check sources and avoid over-trusting a confident-looking result.
Word match versus meaning match
Show why two texts can be close in meaning even when they do not share many words.
01
Query
Start with one search query or sentence to compare.
At the top centre, place a rounded box labelled 'Query: Roads blocked after heavy rain'.
02
Keyword view
Exact or near-exact word overlap is checked. A passage with few shared words may score low even if it means nearly the same thing.
On the left, draw three passage boxes under a heading 'Keyword search'. Highlight matching words in the same colour as the query. Show low, medium and high word-overlap badges.
03
Meaning view
Texts are compared by meaning. A passage can rank high even when the wording is different.
On the right, draw the same three passage boxes under a heading 'Semantic similarity'. Add score bars beside each passage, with the paraphrase passage receiving the highest bar.
04
Result
The best result under semantic similarity is the passage closest in meaning, not necessarily the one with the most shared words.
At the bottom, show two ranked lists side by side. Use arrows from each panel to its ranking. Mark the semantic result with a note 'best meaning match'.
A side-by-side comparison. The left panel shows keyword matching between a query and three passages, with only shared words highlighted. The right panel shows semantic similarity scores for the same query and passages, ranking the passage with closest meaning highest. Read from top to bottom in each panel, then compare the final rankings.
Hover or tap each passage to reveal why it ranked where it did: shared words on the left, similarity score and short meaning note on the right.
Describe this without the diagram
The diagram compares two ways of matching a query to passages. The query is 'Roads blocked after heavy rain'. In the keyword panel, passages rank mainly by shared words. A passage using different words, such as 'transport routes were closed after severe rainfall', ranks lower because few words match exactly. In the semantic panel, that same passage ranks highest because its meaning is closest. The main point is that meaning-based comparison can find relevant text even when wording changes.
With reduced motion: A static side-by-side graphic with the same query, passages, highlights and final rankings, without hover animation. Explanatory labels are always visible.
Semantic similarity compares texts by meaning rather than by exact word overlap. The input is usually two texts, such as two sentences, a question and a paragraph, or a search query and many documents. The output is a score or ranking that shows which texts are closest in meaning.
A common way to do this is to turn each text into an embedding, which is a list of numbers designed to place similar meanings near each other. The system then compares those number lists and gives higher scores to items that are closer together. One common comparison method is cosine similarity, but semantic similarity is the broader idea, not the specific formula.
This matters because people often say the same thing in different words. A search for “flooded roads after storm” may still match a passage that says “transport routes were blocked after heavy rain”. Keyword search may miss that connection if the exact words do not overlap.
Semantic similarity does not prove that two texts are equally correct, complete or safe to use. It only estimates closeness of meaning based on patterns learned from data. That is why results still need checking, especially in high-stakes work.
How “Finding the right note in a mixed pile” maps onto the real thing
| In the story | In the AI system |
|---|---|
| Amina sorting notes into trays | A system grouping or ranking texts by closeness of meaning |
| Different wording about evening sessions | Texts that are semantically similar despite using different words |
| The chair note placed in another tray | A text judged less similar because it expresses a different idea |
A worked example
The associate expert uses an approved search tool to locate documents related to an implementing partner's final progress report before completing the records inventory.
Starting from: Search text: “final report on the partner’s vocational training activities”
- The tool encodes the search text and permitted document titles or extracts as embeddings.
- It compares candidates including “completion narrative for skills activities”, “quarterly expenditure forecast” and “office seating plan”.
- A similarity function ranks the completion narrative highest because its embedding is closest in meaning to the search text, despite the different wording.
- The associate expert opens the result and checks its date, status and metadata rather than treating the similarity ranking as proof that it is the required record.
Result: The related document is added to the inventory for review. No record is merged, reclassified or deleted solely because the system ranked it as semantically similar.
In a United Nations workplace
During a country office records retention review, a newly arrived associate expert supporting the programme unit must prepare a file inventory by close of business. An approved internal search tool identifies concept notes, progress reports and closure documents that discuss the same activity even though their titles and wording differ.
Semantic similarity can surface related records that keyword search misses. This helps the associate expert assemble the file for review, particularly where document titles reflect several years of creative naming conventions.
Before you try thisUse only an approved tool within the records repository and existing access permissions. Do not upload personal, HR, procurement-sensitive or otherwise confidential records to an external service, because document text, embeddings or query logs may expose protected content. Similarity alone must not determine retention, deletion or access classification.
Where you may hear it
- During a vendor demo for an enterprise search tool
- At an ICT governance board review of document retrieval
- While configuring a chatbot that cites internal sources
- During a records retention review of a shared drive
Avoid confusion
Avoid confusion
Often confused with
Cosine similarity
A measure of how closely two non-zero vectors point in the same direction, commonly used to compare embeddings in search.
Keyword search
A search method that finds documents by matching query words to indexed text and ranking the matches, often with scoring methods such as BM25.
Common misconceptions
Common misconception: It just means counting shared words.
Correction: Word overlap is a different method, often called keyword search. Semantic similarity tries to capture closeness of meaning, so two texts can score as similar even when they use different words.
Common misconception: A high similarity score means the answer is true.
Correction: The score only reflects estimated meaning closeness between texts. A passage can be very similar to your question and still be outdated, misleading or wrong, so human checking and source review still matter.
Common misconception: Semantic similarity and cosine similarity are the same thing.
Correction: Semantic similarity is the overall concept of comparing meaning. Cosine similarity is one mathematical way to compare text representations such as embeddings. Systems may use cosine similarity, but the terms are not interchangeable.
Quick check
Which example best shows semantic similarity rather than simple keyword matching?
The second option shows two texts with different wording but similar meaning. That is the main idea. Exact word overlap, document length and recency are different signals.
Go deeper
Go deeper
The simple version
It is a way to estimate whether two pieces of text mean nearly the same thing, even when they do not use the same words.
The technical version
In practice, semantic similarity is often estimated by encoding each text into an embedding, a numeric representation that places semantically related texts near one another in a vector space. A similarity function, often cosine similarity, is then used to score pairs of vectors. Higher scores suggest greater closeness of meaning. This supports tasks such as semantic search, retrieval and ranking. The exact behaviour depends on the embedding model, the text length, the domain, the language and the comparison method, so the score is an estimate rather than a proof of equivalence.
Reviewed external resources
EncyclopaediaSemantic similarity (opens in a new tab)Wikipedia
This Wikipedia page gives a broad overview of the concept and suits readers who want a general starting point.
Intermediatelink checked 2026-07-31
CourseEmbeddings (opens in a new tab)Google
This course resource explains embeddings, which are commonly used to estimate meaning similarity, and suits beginners who want the mechanism in simple steps.
Intermediatelink checked 2026-07-31
InteractiveEmbedding Projector (opens in a new tab)TensorFlow
This interactive tool lets readers explore how items can sit near or far apart in an embedding space, which suits visual learners.
Starterlink checked 2026-07-31
VideoVideo explanations: Semantic similarity (opens in a new tab)YouTube search
This YouTube search results page helps readers find different video explanations of semantic similarity and suits people who prefer to compare teaching styles.
search results, not one pagelink checked 2026-07-31
DiscussionCommunity discussion: Semantic similarity (opens in a new tab)Reddit search
This Reddit search results page shows community discussions and questions about semantic similarity and suits readers who want informal examples and common points of confusion.
search results, not one pagelink checked 2026-07-31
Where this came from
Drafted with a language model through an offline pipeline, checked against this site's schema, and every external link above fetched to see whether it answers. Treat it as a starting point and check anything that matters against the primary sources. How this site is built and what that means.
The one thing to remember
Semantic similarity ranks text by estimated closeness of meaning, not identical wording. A high score is a retrieval clue, not proof that two records are equivalent.